Duplicate from tngtech/DeepSeek-TNG-R1T2-Chimera

Co-authored-by: Robert Dahlke <rbrt@users.noreply.huggingface.co>

.gitattributes

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+intelligence_score_vs_output_tokens.png filter=lfs diff=lfs merge=lfs -text

LICENSE.DeepSeek

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+MIT License
+
+Copyright (c) 2023 DeepSeek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+library_name: transformers
+base_model:
+- deepseek-ai/DeepSeek-R1-0528
+- deepseek-ai/DeepSeek-R1
+- deepseek-ai/DeepSeek-V3-0324
+pipeline_tag: text-generation
+---
+# DeepSeek-TNG-R1T2-Chimera
+
+<div align="center">
+<img src="https://354918363417-runtime-assets.s3.eu-central-1.amazonaws.com/company_logo_light.svg"
+     alt="TNG Logo" 
+     width="400"
+     style="display: inline-block; vertical-align: middle;"/>
+</div>
+<br>
+<div align="center">
+  <a href="https://huggingface.co/tngtech/DeepSeek-TNG-R1T2-Chimera/blob/main/LICENSE.DeepSeek" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<br>
+<div align="center">
+    <img alt="Intelligence Score" src="intelligence_score_vs_output_tokens.png" style="display: inline-block; vertical-align: middle;" width="750"/>
+    <figcaption><a href="https://x.com/tngtech/status/1940531045432283412">Release Announcement on X</a></figcaption>
+</div>
+
+
+## Assembly of Experts Chimera model constructed with the DeepSeek [R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528), [R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) and [V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) parent models
+
+We present our new **DeepSeek-TNG R1T2 Chimera** 671B model, the first successor to our original [*DeepSeek R1T Chimera*](https://huggingface.co/tngtech/DeepSeek-R1T-Chimera) that was released on April 26th. Unlike the original Chimera, which was based on the *two parent models* V3-0324 and R1, the new Chimera is a **Tri-Mind** *with three parents*, namely additionally R1-0528. It is constructed using the Assembly of Experts-method with relatively fine-granular direct brain edits. This more refined assembly allowed, among other improvements, the fixing of the &lt;think&gt; token consistency issue, which was a weakness of R1T and is now solved for R1T2. 
+
+**Sweet spot**
+
+R1T2 operates at a new sweet spot in intelligence vs. output token length. It appears to be...
+
+- about **20% faster than** the regular **R1**, and more than **twice as fast as R1-0528**
+- significantly **more intelligent than** the regular **R1** in benchmarks such as **GPQA**, **AIME-24** and **Aider Polyglot**
+- much **more intelligent** and also **think-token consistent** compared to the first **R1T Chimera** 0426
+- and generally well-behaved and a **nice persona** to talk to, even without any system prompt.
+
+**Recommendations for your model decision**
+
+*R1T2* compared...
+- *vs R1:* We hope that R1T2 is a very desirable, almost universally **better drop-in replacement for R1**
+- *vs R1-0528:* R1T2 is a much **cheaper alternative to the full R1-0528**, if the full 0528-level intelligence is not required
+- *vs R1T:* R1T2 is usually **recommended over R1T**, unless the specific personality of R1T was optimal, the think-token issue not important, or R1T's higher speed crucial
+- *vs V3-0324:* V3 is so much faster that if you can live with the **lower intelligence, take V3**, however, if you **need reasoning, R1T2** is the go-to model
+
+**Limitations**
+
+- **R1-0528** is thinking much longer, but also is achieving **better hard benchmark results** than R1T2
+- As measured by SpeechMap.ai (courtesy of xlr8harder), **R1T2** is significantly **more reserved** than R1T, but not as much as R1-0528
+- When switching from R1T to R1T2 development, we changed from AIME24 and MT-Bench to AIME24, AIME25 and GPQA-Diamond for the intelligence score. With the new benchmark set, there is a larger score difference between R1 and the original R1T Chimera than published earlier.
+- Function calling is supported in general, but both vLLM and SGLang currently require some specific adaptions, see the section below.
+
+**Evaluation results**
+
+Evaluation was performed using the evalchemy framework (pass@1 averaged over 10/5 runs for AIME/GPQAD, at a temperature of 0.6).
+We report measured benchmark results for our R1T2, R1T models and published benchmark results for V3-0324, R1, R1-0528.
+
+|                                    | R1T2 |  R1T | V3-0324 |   R1 | R1-0528 | Comment |
+|:-----------------------------------|-----:|-----:|--------:|-----:|--------:|:--------|
+| AIME-24                            | 82.3 | 74.7 |    59.4 | 79.8 |    91.4 |         |
+| AIME-25                            | 70.0 | 58.3 |    49.6 | 70.0 |    87.5 | V3-0324 source: AIME-25 measured by us |
+| GPQA-Diamond                       | 77.9 | 72.0 |    68.4 | 71.5 |    81.0 |         |
+| Aider Polyglot                     | 64.4 | 48.4 |    44.9 | 52.0 |    71.6 | R1T2 source: Aider discord, t=0.75 |
+| EQ-Bench Longform Creative Writing | 76.4 |  ./. |    78.1 | 74.6 |    78.9 | see [EQ Bench](https://eqbench.com/creative_writing_longform.html)  |
+
+## Technological background
+
+For details on the AoE construction process, you can read our [Paper on arXiV](https://arxiv.org/abs/2506.14794).
+
+**Runtime parameter settings**
+
+- Most of our evaluation was done with a maximum context size of 60,000 tokens.
+  With a context size of 130,000 tokens, the model proved very helpful in interpreting very long debug logs. Long-context testing was less extensive, though.
+- We're running the model using vLLM on 8xH200 and MI325X nodes, additionally we've tested the model using SGLang, which is also used by [chutes.ai](https://chutes.ai/app/chute/4fa0c7f5-82f7-59d1-8996-661bb778893d).
+- For SGLang, we recommend using versions >= v0.4.8 in combination with argument `--reasoning-parser qwen3` to properly handle rare cases when the model skips the `<think>` reasoning step. 
+
+
+### Function calling
+
+R1T2 does support function calling using an updated chat template (since 01 Aug 2025). However, neither vLLM nor SGLang provide an R1T2-compatible tool call parser natively but require some adaptions.
+
+_vLLM:_
+
+For function calling with vLLM, a new tool parser is required. While we opened [a PR to vLLM](https://github.com/vllm-project/vllm/pull/22074) to include an R1T2-compatible tool parser off-the-shelf, we also ship the tool parser file `tool_parser_vllm.py` within this repository. 
+With this file, tool calling can be enabled via
+```
+--tool-parser-plugin <ABSOLUTE_MODEL_SNAPSHOT_PATH>/tool_parser_vllm.py  \
+--tool-call-parser tng_r1t2
+```
+
+Here, put in the path to the snapshot folder such as `~/.cache/huggingface/hub/models--tngtech--DeepSeek-TNG-R1T2-Chimera/snapshots/SNAPSHOT/tool_parser_vllm.py`
+
+_SGLang:_
+
+Tool call support for R1T2 requires a recent SGLang version >= v0.4.10 (alternatively, you need to patch [this bugfix for the reasoning parser](https://github.com/sgl-project/sglang/pull/8606) for older versions of SGLang).
+
+An R1T2-compatible tool call parser will be added with [this PR to SGLang](https://github.com/sgl-project/sglang/pull/8672). 
+Unfortunately, and unlike vLLM, there is no simple plugin system for tool call parsers in SGLang. 
+Until our PR is merged an relased with a new SGLang version, you can still install it manually by patching your SGLang source code as outlined in the PR:
+The new tool call parser must be added and registered (so in total one file must be added, a second one edited, see [details here](https://github.com/sgl-project/sglang/pull/8672/files)).
+
+Once the SGLang installation has been updated correctly, tool calling with R1T2 can be activated by starting SGLang with
+
+```
+--tool-call-parser tng_r1t2
+```
+
+
+## Model Details
+
+- **Architecture**: DeepSeek-MoE transformer-based language model
+- **Combination Method**: Assembly of Experts from the three DeepSeek parent models R1-0528, R1 and V3-0324
+- **Release Date**: 2025-07-02
+- **Design Team**: Robert Dahlke, Henrik Klagges, Benjamin Merkel, Fabian Klemm and David Reiss, Munich, Germany
+- **Extra Thanks**: Big thanks to DeepSeek for their great models and open-source generosity, and to the other researchers that have published on model merging methodologies.
+
+
+## Use, Out-of-scope Use, Other Limitations, Risks, Recommendations et al.
+Regarding the R1T/R1T2-Chimeras, we ask you to follow the careful guidelines that Microsoft has created for their "MAI-DS-R1" DeepSeek-based model. 
+These professional guidelines are available [here on Hugging Face](https://huggingface.co/microsoft/MAI-DS-R1).
+
+## EU AI Act
+
+Due to the strict new guidelines of the EU AI Act that take effect on August 2nd 2025, we recommend that each R1T/R1T2 user in the EU either familiarizes themselves with these requirements and assess their compliance, or ceases using the model in the EU after August 1st, 2025.
+
+## Contact, especially for your user feedback
+
+Please give us your feedback, especially if you find deficiencies in the model:
+- Email: research@tngtech.com
+- X.com: @tngtech
+
+## Citation
+
+```
+@misc{tng_technology_consulting_gmbh_2025_07_02,
+	author       = { TNG Technology Consulting GmbH },
+	title        = { DeepSeek-TNG-R1T2-Chimera },
+	year         = 2025,
+    month        = { July },
+	url          = { https://huggingface.co/tngtech/DeepSeek-TNG-R1T2-Chimera },
+	doi          = { 10.57967/hf/5950 },
+	publisher    = { Hugging Face }
+}
+```

config.json

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+{
+  "architectures": [
+    "DeepseekV3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_deepseek.DeepseekV3Config",
+    "AutoModel": "modeling_deepseek.DeepseekV3Model",
+    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 3,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 18432,
+  "kv_lora_rank": 512,
+  "max_position_embeddings": 163840,
+  "model_type": "deepseek_v3",
+  "moe_intermediate_size": 2048,
+  "moe_layer_freq": 1,
+  "n_group": 8,
+  "n_routed_experts": 256,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 61,
+  "num_key_value_heads": 128,
+  "num_nextn_predict_layers": 1,
+  "q_lora_rank": 1536,
+  "qk_nope_head_dim": 128,
+  "qk_rope_head_dim": 64,
+  "quantization_config": {
+    "activation_scheme": "dynamic",
+    "fmt": "e4m3",
+    "quant_method": "fp8",
+    "weight_block_size": [
+      128,
+      128
+    ]
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 40,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "type": "yarn"
+  },
+  "rope_theta": 10000,
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sigmoid",
+  "tie_word_embeddings": false,
+  "topk_group": 4,
+  "topk_method": "noaux_tc",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "v_head_dim": 128,
+  "vocab_size": 129280
+}

configuration_deepseek.py

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+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+class DeepseekV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the DeepSeek-V3.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 129280):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the DeepSeekV3 Model.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
+    >>> # Initializing a Deepseek-V3 style configuration
+    >>> configuration = DeepseekV3Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size = 2048,
+        num_hidden_layers=61,
+        num_nextn_predict_layers=1,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts = 1,
+        n_routed_experts = 256,
+        ep_size = 1,
+        routed_scaling_factor = 2.5,
+        kv_lora_rank = 512,
+        q_lora_rank = 1536,
+        qk_rope_head_dim = 64,
+        v_head_dim = 128,
+        qk_nope_head_dim = 128,
+        topk_method = 'noaux_tc',
+        n_group = 8,
+        topk_group = 4,
+        num_experts_per_tok = 8,
+        moe_layer_freq = 1,
+        first_k_dense_replace = 3,
+        norm_topk_prob = True,
+        scoring_func = 'sigmoid',
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
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+            bos_token_id=bos_token_id,
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